With the development of Internet technology, users can obtain more and more content (e.g., network objects) from websites. As users browse websites to choose network objects, website recommendation systems typically play an important role. For example, users without clear demands are likely to directly choose network objects recommended by the recommendation systems of the websites. An efficient recommendation system not only can be used conveniently by users, increasing the value of a website, but also, more importantly, can reduce aimless behavior of the users, such as aimless browsing and clicking, which helps reduce the burden of website servers and saving network bandwidth resources.
At present, more and more websites are starting to have their own recommendation systems. Many websites adopt an off-line recommendation algorithm based on Hadoop Map/Reduce. Map/Reduce conducts data processing mainly by the method of batch processing, and normally reads data from disks. Therefore, the main idea of the off-line recommendation algorithm based on Hadoop Map/Reduce is conducting collaborative calculations based upon historical behavior data of the users from the day before or within a long prior period of time and outputting recommendations according to a calculation result. Such an algorithm often has low recommendation accuracy and poor positive impact, thus failing to meet the requirements of some application scenarios requiring real-time collaboration and fast response.